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Existing optimization decision-making approaches for pavement maintenance and rehabilitation (M&R) ignore the construction length of preventive maintenance (PM) projects, and its negative effects are difficult to be transformed into cost. To address this issue, this study proposes a bi-objective decision-making model that incorporates the problem as the second objective into the two-stage bottom-up approach. The proposed model contains selection of performance indicators, Bayesian neural network-based probabilistic deterioration model, evaluation of initial M&R actions on a segment level, and bi-objective decision-making. It is solved by the enumeration method and the non-dominated sorting genetic algorithm II. Finally, the Pareto solutions are obtained. A solution is an M&R plan, where an initial action (treatment type) is selected for a pavement segment. Among the Pareto solutions, the one with the second objective greater than or equal to and closest to the shortest construction length, is the optimal M&R plan. In addition, compared with the model that converts the problem into a constraint, the proposed model recommends a better plan that can achieve higher performance at lower cost, which validates the strength of the proposed model. Decision-makers can adopt the proposed model to optimize pavement M&R plans that consider the construction length of PM projects. [ABSTRACT FROM AUTHOR]

High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods. [ABSTRACT FROM AUTHOR]

This study explores the temporal instability and non-transferability of the determinants affecting injury severities of pedestrians struck by motorcycles and non-motorcycles. Using the pedestrian-vehicle crash data in Rawalpindi, Pakistan, over three years (2017–2019), three possible crash injury severity categories (minor injury, severe injury, and fatal injury) are estimated using alternative models to account for unobserved heterogeneity. These are a random-parameters multinomial logit (RP-ML) model with heterogeneity in means and variances and a latent-class multinomial logit (LC-ML) model with class probability functions. Temporal instability and non-transferability in the effects of explanatory variables are confirmed using a series of likelihood ratio tests based on the two alternative models. Various variables are observed to determine pedestrian-injury severities, and the estimation results show significant temporal instability and non-transferability in both RP-ML and LC-ML models. However, several explanatory variables produce relatively temporally stable and transferable effects, providing valuable insights to implement effective countermeasures from a long-term perspective. Moreover, out-of-sample predictions are simulated to confirm the temporal instability and non-transferability. At the same time, the LC-ML models produce higher differences for temporal instability and lower differences for non-transferability compared to the RP-ML model. Understanding and depth comparing the estimation results, likelihood ratio tests, and out-of-sample predictions using alternative models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated in terms of temporal instability and non-transferability. [ABSTRACT FROM AUTHOR]

A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017–2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets. [ABSTRACT FROM AUTHOR]

Single and multi-vehicle (SMV) crashes remain a significant issue, causing serious safety and economic concerns, and therefore deserve more attention. Using crash data in the Beijing-Shanghai and Changchun-Shenzhen freeways over the five years (2015–2019), this paper explored the transferability and heterogeneity for crash type (single-vehicle versus multi-vehicle crashes) and spatiotemporal stability of determinants affecting the injury severity. The random parameters logit approach with heterogeneity in means and variances was used to model three possible crash injury severity outcomes (measured by the most severely injured individual in the crash) of no injury, minor injury, and severe injury and identify the determinants in terms of driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. Remarkable differences were observed in the SMV crashes, and the contributing factors also reported considerable temporal and (or) spatial instabilities. The insights of this study should be valuable to help freeway designers and decision-makers understand the contributing mechanism of the factors and develop the proper management strategies and enforcement countermeasures. [ABSTRACT FROM AUTHOR]

It is crucial for drivers to conduct rapid and effective risk perception and response processes when faced with hazardous driving situations. The low pressure and oxygen environment in the plateau results in a greater workload of drivers, contributing to a significant decline in perception and response ability. This study proposes a graph construction approach to model drivers' hazard response modes (HRMs) in plateau areas. A total of 31 drivers (23 males) aged 21 to 55 years (M [age] = 28.0 years, M [driving experience] = 6.5 years) were recruited to participate in four hazard perception experiments using a UC-WIN/ROAD driving simulator. The experiments were successively conducted in five cities with different altitudes, including Nanjing (50 m), Nyingchi (2,995 m), Lhasa (3,650 m), Nagqu (4,460 m), and Yanghu Scenic Spot (4,998 m). Then, according to the graph construction approach, four HRMs for drivers were extracted. In addition, two series of generalized linear models were proposed to analyze the relationships between the perception reaction time (PRT), HRM, altitude, age, acclimation period, gender, and driving experience. The effects of significant variables, including scenario types, altitude, acclimation period, driving experience, and gender, were used in the construction of HRM and risk perception ability of plateau drivers. These results showed that constructing HRMs to model the driving styles of plateau drivers is feasible and effective, enabling future driving assistance systems to be better customized for drivers in such a particular condition. [ABSTRACT FROM AUTHOR]

The process of pavement deterioration involves uncertainties, and neural networks have been widely used in pavement performance prediction due to their high accuracy. However, the overwhelming majority of current performance prediction models based on neural networks are deterministic. Therefore, this study combined Bayesian theory and neural networks to establish a Bayesian neural network (BNN)-based probabilistic model for predicting pavement deterioration. The proposed model was built on the pavement data in Shanxi Province, China. This study first refined data using the K-Nearest Neighbour and empirical methods, and then selected input features based on correlation coefficient methods. Using the refined data, the deterministic neural network model was established to obtain the prior probability distribution of weights, and then the BNN-based probabilistic model was developed. Compared with the sole neural network model, the BNN-based model not only retains comparable prediction accuracy to the neural network model, but also incorporates uncertainties. The BNN-based model is also theoretically superior to the Markov-based probabilistic model because the former can incorporate all factors and does not need to classify performance values into states. The BNN-based model shall provide more reliable prediction results of pavement deterioration and help engineers make more reasonable maintenance decisions. [ABSTRACT FROM AUTHOR]

To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017–2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures. [ABSTRACT FROM AUTHOR]

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors. [ABSTRACT FROM AUTHOR]

Objective: In the last few decades, the growing popularity of e-bikes in China has raised public concerns regarding an increasing number of fatalities and injuries involving e-bikes. Although previous studies have explored the impacts of personality on driving behaviors of automobile drivers and motorcyclists, little attention has been paid to safety-related issues involving e-bikers from the aspect of their personality traits and sociocognitive variables. The aim of this study was to examine the effects of personality on e-bikers' risk-taking behaviors and test the effectiveness of the model proposed by Ulleberg and Rundmo (2003) among e-bikers.Methods: Four hundred and five Chinese e-bikers aged 16 to 61 completed a self-administrated questionnaire, which included questions investigating their demographics, personality traits (anger, altruism, sensation-seeking, normlessness), risk perceptions, safety attitudes, and risky riding behaviors. The reliability and validity of all scales were first examined through reliability analysis and principal component analysis, respectively, and a structural equation model was developed and fitted to test the relationships among e-bikers' personality traits, risk perceptions, safety attitudes, and risky riding.Results: A satisfactory level of reliability and validity was reached for all variables. Anger, altruism, sensation-seeking, and normlessness were all significantly related to e-bikers' risk perceptions and unsafe riding, and only altruism correlated significantly to safety attitude. For 2 sociocognitive variables, safety attitudes was directly and negatively related to respondents' risky riding, and risk perception only exerted impacts on riding behaviors by affecting safety attitudes.Conclusions: Personality traits of e-bikers impacted their riding behaviors both directly and indirectly, and sociocognitive variables played an intermediate role in the personality-behavior relationship. The results revealed the importance of personality traits in influencing e-bikers' risky riding and also verified the applicability of the personality-behavior model proposed by Ulleberg and Rundmo (2003) among e-bikers. The findings of this study may provide an empirical basis for evidence-based safety interventions for e-bikers in China. [ABSTRACT FROM AUTHOR]

Objective: Illegal parking in emergency lanes (paved highway shoulders) is becoming a serious road safety issue in China. The aim of this study was to (1) examine the utility of the theory of planned behavior (TPB) extended with descriptive norms, past behavior, facilitating and deterring circumstances, sensation seeking, and invulnerability in predicting Chinese drivers' intentions toward illegal emergency lane parking; (2) investigate whether respondents' demographic characteristics would impact their views toward the behavior and predictive patterns of intentions; and (3) identify significant predictors of intentions.Methods: In this cross-sectional study, eligible respondents were all qualified Chinese drivers. A self-administered questionnaire was employed to collect data, including demographic information, descriptive norms, past behavior, facilitating and deterring circumstances, sensation-seeking, and scenario-based invulnerability combined with TPB constructs. Descriptive statistics, multivariate analyses of variance (MANOVAs), and a series of hierarchical multiple linear regression analyses were conducted in SPSS.Results: A total of 435 qualified drivers (234 males and 201 females) with a mean age of 35.2 years (SD =10.3) were included in analysis. The descriptive analysis showed that most participants reported weak intentions (M = 2.35) to park illegally in emergency lanes with negative attitude (M = 3.19), low perceived support (M = 2.91), and high control (M = 5.08) over the behavior. The model succeeded in explaining 64% of the variance in intentions for the whole sample, and principal TPB components accounted for 21% of variance in intentions after demographic variables were controlled for. MANOVAs revealed that significant differences of respondents' opinions toward illegal emergency lane parking were only found between better educated drivers (with college education background) and less-educated ones. Separate regression analyses revealed that the predictive pattern of better educated participants also differed significantly from that of less-educated ones.Conclusion: The study revealed that perceived behavioral control, past behavior, facilitating circumstance, and invulnerability emerged as consistently significant predictors of Chinese drivers' intentions to park illegally in emergency lanes. Findings of this study may have some practical implications in developing multifaceted interventions or education processes for illegal emergency lane parking in China. [ABSTRACT FROM AUTHOR]